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used to measure motion and deformation. It provides comprehensive full-field deformation data, essential for analysing complex materials, structures and model validation. The DIC community has developed
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complex input. For instance, in physics-informed ML, in addition to data examples used by a standard ML setup, domain knowledge serves as an additional input. It can be in an explicit form of rigorous
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highly complex task, as sparse instrumentation does not guarantee direct sensing at all fatigue-critical locations of the substructure’s primary steel. Analytical solutions – so-called virtual sensing
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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providers, recursively forming complex supply chains that are difficult to analyze. As a result, operators may unknowingly introduce technical, operational, and regulatory risks – especially in critical
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scholarship, in partnership with the Australian Office of National Intelligence, will be awarded to an outstanding applicant interested in connecting spatial and spectral information to understand complex
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deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature
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Water, the student will use the Complex Value Optimisation for Resource Recovery (CVORR) methodology to design a practical decision-support tool for identifying, quantifying, and advancing circular
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-22 eV or better, and powerfully test the Standard Model of particle physics. They further constrain CP-violating new physics at scales of 10-100 TeV, far beyond the reach of the LHC. The TUM and the
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– depending on the successful candidate’s background and interests. Your tasks: Develop new exact and approximation algorithms and perform complexity analyses for optimization problems on (temporal) graphs